| Season | Team | League | GP | G | A | Pts | PPG | NCAAe-PPG | Age-Adj | D3e-PPG | Age-Adj |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 2021-22 | Burlington Cougars | OJHL | 46 | 3 | 13 | 16 | 0.348 | 0.0852 | 0.0864 | 0.2381 | 0.2414 |
| 2022-23 | Burlington Cougars | OJHL | 38 | 4 | 19 | 23 | 0.605 | 0.1484 | 0.1429 | 0.4143 | 0.3988 |
| 2023-24 | Burlington Cougars | OJHL | 48 | 6 | 25 | 31 | 0.646 | 0.1583 | 0.1442 | 0.4421 | 0.4026 |
| Season | School | Div | Conference | Year | GP | G | A | Pts | PPG |
|---|---|---|---|---|---|---|---|---|---|
| 2025-26 | Salve Regina | D3 | CNE | SO | 24 | 2 | 4 | 6 | 0.250 |
| 2024-25 | Salve Regina | D3 | CNE | — | 5 | 0 | 1 | 1 | 0.200 |
How to read this: NCAAe and D3e factors convert a player's junior PPG into expected NCAA scoring at the D1 or D3 level. Harder conferences → lower projected PPG for the same player. A strong junior player (e.g. USHL 0.90 PPG) will project much higher in NESCAC than Big Ten because the D3 scoring environment is lower-difficulty.
Strength factor: conferences above 1.0 are harder than average; below 1.0 are easier. The formula is: Base NCAAe PPG ÷ Conference Strength = Projected PPG.